add hypergraph vertex weight (v_weight and W_v)

Author: yifanfeng97

dhg/structure/hypergraphs/hypergraph.py

@@ -22,6 +22,7 @@ class Hypergraph(BaseHypergraph):
         ``num_v`` (``int``): The number of vertices in the hypergraph.
         ``e_list`` (``Union[List[int], List[List[int]]]``, optional): A list of hyperedges describes how the vertices point to the hyperedges. Defaults to ``None``.
         ``e_weight`` (``Union[float, List[float]]``, optional): A list of weights for hyperedges. If set to ``None``, the value ``1`` is used for all hyperedges. Defaults to ``None``.
+        ``v_weight`` (``Union[List[float]]``, optional): A list of weights for vertices. If set to ``None``, the value ``1`` is used for all vertices. Defaults to ``None``.
         ``merge_op`` (``str``): The operation to merge those conflicting hyperedges in the same hyperedge group, which can be ``'mean'``, ``'sum'`` or ``'max'``. Defaults to ``'mean'``.
         ``device`` (``torch.device``, optional): The deivce to store the hypergraph. Defaults to ``torch.device('cpu')``.
     """
@@ -31,10 +32,18 @@ def __init__(
         num_v: int,
         e_list: Optional[Union[List[int], List[List[int]]]] = None,
         e_weight: Optional[Union[float, List[float]]] = None,
+        v_weight: Optional[List[float]] = None,
         merge_op: str = "mean",
         device: torch.device = torch.device("cpu"),
     ):
         super().__init__(num_v, device=device)
+        # init vertex weight
+        if v_weight is None:
+            self._v_weight = [1.0] * self.num_v
+        else:
+            assert len(v_weight) == self.num_v, "The length of vertex weight is not equal to the number of vertices."
+            self._v_weight = v_weight
+        # init hyperedges
         if e_list is not None:
             self.add_hyperedges(e_list, e_weight, merge_op=merge_op)
 
@@ -495,6 +504,12 @@ def v(self) -> List[int]:
         r"""Return the list of vertices.
         """
         return super().v
+    
+    @property
+    def v_weight(self) -> List[float]:
+        r"""Return the list of vertex weights.
+        """
+        return self._v_weight
 
     @property
     def e(self) -> Tuple[List[List[int]], List[float]]:
@@ -634,7 +649,7 @@ def vars_for_DL(self) -> List[str]:
         Sparse Diagnal Matrices:
 
         .. math::
-            \mathbf{W}_e, \mathbf{D}_v, \mathbf{D}_v^{-1}, \mathbf{D}_v^{-\frac{1}{2}}, \mathbf{D}_e, \mathbf{D}_e^{-1},
+            \mathbf{W}_v, \mathbf{W}_e, \mathbf{D}_v, \mathbf{D}_v^{-1}, \mathbf{D}_v^{-\frac{1}{2}}, \mathbf{D}_e, \mathbf{D}_e^{-1},
 
         Vectors:
 
@@ -649,6 +664,7 @@ def vars_for_DL(self) -> List[str]:
             "L_sym",
             "L_rw",
             "L_HGNN",
+            "W_v",
             "W_e",
             "D_v",
             "D_v_neg_1",
@@ -754,14 +770,14 @@ def e2v_weight_of_group(self, group_name: str) -> torch.Tensor:
 
     @property
     def H(self) -> torch.Tensor:
-        r"""Return the hypergraph incidence matrix :math:`\mathbf{H}` with ``torch.Tensor`` format.
+        r"""Return the hypergraph incidence matrix :math:`\mathbf{H}` with ``torch.sparse_coo_tensor`` format.
         """
         if self.cache.get("H") is None:
             self.cache["H"] = self.H_v2e
         return self.cache["H"]
 
     def H_of_group(self, group_name: str) -> torch.Tensor:
-        r"""Return the hypergraph incidence matrix :math:`\mathbf{H}` of the specified hyperedge group with ``torch.Tensor`` format.
+        r"""Return the hypergraph incidence matrix :math:`\mathbf{H}` of the specified hyperedge group with ``torch.sparse_coo_tensor`` format.
 
         Args:
             ``group_name`` (``str``): The name of the specified hyperedge group.
@@ -773,14 +789,14 @@ def H_of_group(self, group_name: str) -> torch.Tensor:
 
     @property
     def H_T(self) -> torch.Tensor:
-        r"""Return the transpose of the hypergraph incidence matrix :math:`\mathbf{H}^\top` with ``torch.Tensor`` format.
+        r"""Return the transpose of the hypergraph incidence matrix :math:`\mathbf{H}^\top` with ``torch.sparse_coo_tensor`` format.
         """
         if self.cache.get("H_T") is None:
             self.cache["H_T"] = self.H.t()
         return self.cache["H_T"]
 
     def H_T_of_group(self, group_name: str) -> torch.Tensor:
-        r"""Return the transpose of the hypergraph incidence matrix :math:`\mathbf{H}^\top` of the specified hyperedge group with ``torch.Tensor`` format.
+        r"""Return the transpose of the hypergraph incidence matrix :math:`\mathbf{H}^\top` of the specified hyperedge group with ``torch.sparse_coo_tensor`` format.
 
         Args:
             ``group_name`` (``str``): The name of the specified hyperedge group.
@@ -789,10 +805,25 @@ def H_T_of_group(self, group_name: str) -> torch.Tensor:
         if self.group_cache[group_name].get("H_T") is None:
             self.group_cache[group_name]["H_T"] = self.H_of_group(group_name).t()
         return self.group_cache[group_name]["H_T"]
+    
+    @property
+    def W_v(self) -> torch.Tensor:
+        r"""Return the weight matrix :math:`\mathbf{W}_v` of vertices with ``torch.sparse_coo_tensor`` format.
+        """
+        if self.cache.get("W_v") is None:
+            _tmp = torch.Tensor(self.v_weight)
+            _num_v = _tmp.size(0)
+            self.cache["W_v"] = torch.sparse_coo_tensor(
+                torch.arange(0, _num_v).view(1, -1).repeat(2, 1),
+                _tmp,
+                torch.Size([_num_v, _num_v]),
+                device=self.device,
+            ).coalesce()
+        return self.cache["W_v"]
 
     @property
     def W_e(self) -> torch.Tensor:
-        r"""Return the weight matrix :math:`\mathbf{W}_e` of hyperedges with ``torch.Tensor`` format.
+        r"""Return the weight matrix :math:`\mathbf{W}_e` of hyperedges with ``torch.sparse_coo_tensor`` format.
         """
         if self.cache.get("W_e") is None:
             _tmp = [self.W_e_of_group(name)._values().clone() for name in self.group_names]
@@ -807,7 +838,7 @@ def W_e(self) -> torch.Tensor:
         return self.cache["W_e"]
 
     def W_e_of_group(self, group_name: str) -> torch.Tensor:
-        r"""Return the weight matrix :math:`\mathbf{W}_e` of hyperedges of the specified hyperedge group with ``torch.Tensor`` format.
+        r"""Return the weight matrix :math:`\mathbf{W}_e` of hyperedges of the specified hyperedge group with ``torch.sparse_coo_tensor`` format.
 
         Args:
             ``group_name`` (``str``): The name of the specified hyperedge group.

tests/structure/test_hypergraph.py

@@ -449,6 +449,12 @@ def test_H_T_group(g1):
     assert (g1.H_T_of_group("knn").to_dense().cpu() == torch.tensor([[1, 0, 0, 0, 1, 1]])).all()
 
 
+def test_W_v(g2):
+    assert (g2.W_v.cpu()._values() == torch.tensor([1, 1, 1, 1, 1])).all()
+    hg = Hypergraph(5, [[1, 2], [0, 2, 3, 4]], v_weight=[0.1, 1, 2, 1, 1])
+    assert (hg.W_v.cpu()._values() == torch.tensor([0.1, 1, 2, 1, 1])).all()
+
+
 def test_W_e(g2):
     assert (g2.W_e.cpu()._values() == torch.tensor([0.5, 1, 0.5, 1, 0.5])).all()